import tensorflow as tf
import numpy as np

x_data = np.random.rand(100)
y_data = x_data*0.1+0.2

b = tf.Variable(0.)
k = tf.Variable(0.)
y = k*x_data + b

loss = tf.reduce_mean(tf.square(y_data-y))
optimizer = tf.train.GradientDescentOptimizer(0.2)

train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(200):
        sess.run(train)
        if step%20 == 0:
            # print(step)
            print(step, sess.run([k, b]))